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Important stuff was said

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Basic Shortcuts Summary

These are in my opinion, the most important shortcut keys to remember in R: * OPTION/ALT+- creates the assignment operator <- * CMD/CTRL+SHIFT+m creates the pipe operator %>% very important for tidyverse * CMD/CTRL+OPTION/ALT+i inserts a code chunk * CMD/CTRL+SHIFT+ENTER executes all code within a chunk

library(tidyverse) #to load in tidyverse library
## ── Attaching packages ─────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 2.2.1     ✔ purrr   0.2.4
## ✔ tibble  1.4.2     ✔ dplyr   0.7.4
## ✔ tidyr   0.8.0     ✔ stringr 1.3.1
## ✔ readr   1.1.1     ✔ forcats 0.3.0
## ── Conflicts ────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
#create a tibble of fake data
fake_data <- tibble(
  Subject = rep(seq(1:10),2), #repeat a sequence from 1 to 10, twice
  Condition = c(rep("easy", 10), rep("hard", 10)), 
  Fake_RT = c(rnorm(10, mean = 250, sd = 2.5), rnorm(10, mean = 294, sd = 2.5))
)
print(fake_data)
## # A tibble: 20 x 3
##    Subject Condition Fake_RT
##      <int> <chr>       <dbl>
##  1       1 easy         245.
##  2       2 easy         251.
##  3       3 easy         250.
##  4       4 easy         249.
##  5       5 easy         250.
##  6       6 easy         246.
##  7       7 easy         255.
##  8       8 easy         251.
##  9       9 easy         250.
## 10      10 easy         251.
## 11       1 hard         294.
## 12       2 hard         296.
## 13       3 hard         295.
## 14       4 hard         295.
## 15       5 hard         296.
## 16       6 hard         298.
## 17       7 hard         291.
## 18       8 hard         296.
## 19       9 hard         293.
## 20      10 hard         295.

Now let’s do something very simple with tidyverse in a code chunk. Let’s summarize the mean and sd for our fake data by the levels of Condition:

group_fake_data <- fake_data %>% 
  group_by(Condition) %>% 
  summarize(meanRT = mean(Fake_RT), sdRT = sd(Fake_RT))
print(group_fake_data)
## # A tibble: 2 x 3
##   Condition meanRT  sdRT
##   <chr>      <dbl> <dbl>
## 1 easy        250.  2.71
## 2 hard        295.  1.81

And let’s end by running a paired t-test, since there are two data points per Participant, one for the easy condition and one for the hard condition.

#formula method for paired t-test
t.test(Fake_RT ~ Condition, data = fake_data, paired = TRUE)
## 
##  Paired t-test
## 
## data:  Fake_RT by Condition
## t = -36.35, df = 9, p-value = 4.469e-11
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -47.79515 -42.19487
## sample estimates:
## mean of the differences 
##               -44.99501
#indexing method for paired t-test
t.test(fake_data$Fake_RT[fake_data$Condition == "easy"], fake_data$Fake_RT[fake_data$Condition == "hard"], paired = TRUE)
## 
##  Paired t-test
## 
## data:  fake_data$Fake_RT[fake_data$Condition == "easy"] and fake_data$Fake_RT[fake_data$Condition == "hard"]
## t = -36.35, df = 9, p-value = 4.469e-11
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -47.79515 -42.19487
## sample estimates:
## mean of the differences 
##               -44.99501

You see that in both instances of running the t.test() that we got the same results. Because all of our dependent variable is contained in one column, I would recommend using the formula syntax method. The indexing method is more helpful when you have your dependent variables split across two columns.